• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.118-127
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• 2020

The paper introduces trends and methodological challenges of quantitative Korean text analysis by using the case studies of academic and news media articles on "migration" and "immigration" within the periods of 2017-2019. The quantitative text analysis based on natural language processing technology (NLP) and this became an essential tool for social science. It is a part of data science that converts documents into structured data and performs hypothesis discovery and verification as the data and visualize data. Furthermore, we examed the commonly applied social scientific statistical models of quantitative text analysis by using Natural Language Processing (NLP) with R programming and Quanteda.

• Language and Information
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• v.14 no.2
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• pp.131-157
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• 2010

This article reports an attempt to perform a similarity analysis on a collection of 25 culinary manuscripts in Chosun periods using a set of quantitative text analysis methods. Historical culinary texts are valuable resources for linguistic, historic, and cultural studies. We consider the similarity of two texts as the distributional similarities of the functional components of the texts. In the case of culinary texts, text elements such as food names, cooking methods, and ingredients are regarded as functional components. We derive the similarity information from the distributional characteristics of the two key functional components, cooking methods and ingredients. The results are also quantified and visualized to achieve a better understanding of the properties of the individual texts and the collection of the texts as a whole.

• Informatization Policy
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• v.25 no.4
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• pp.84-107
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• 2018

The existing research on domestic e-government trends in Korea has weaknesses in that it depends only on qualitative research methods. Therefore, a quantitative analysis was conducted through this study as of September 2018 based on the data from 1996 to 2017. A total of seven research topics were derived from text mining, of which the network centrality of the framework and public policy effect were identified as highly significant. The results of this study provide academic and policy implications for the development of e-government. including that using a quantitative analysis method instead of a qualitative method contributes to ensuring relative objectivity and diversity of learning.

• Speech Sciences
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• v.9 no.1
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• pp.149-171
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• 2002

After establishing the rules for the phonetic transcription in French, quantitative analysis on the given text, Waiting for Godot, is performed. Analyzing the text by investigating the influence of phoneme distribution is very interesting in the phonostylistic point of view. Since the phonetic transcription rules are useful for its automation, the rules are carefully established in this paper. From the results of the phonetic transcription, we can investigate the distribution of individual phonemes and the different phoneme groups between dialogues and scenery indications for various characters.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.2
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• pp.53-60
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• 2012

In this paper, we present new methods for identifying keywords for foresight topics that utilize the internet and textmining techniques to draw objective and quantified information that support experts' qualitative opinions and evaluations in foresight. Furthermore, by applying this fabricated procedure, we have derived keywords to analyze priorities in architectural engineering. Not much difference between qualitative methods of experts and quantitative methods such as text mining has been observed from comparison between technologies derived via qualitative method from "The Science Technology Vision" (control group). Therefore, as a quantitative tool useful for drawing keywords for foresight, textmining can supplement quantitative analysis by experts. In addition, depending on the level and type of raw data, text mining can bring better results in deriving foresight keywords. For this reason, research activities accommodating Internet search results and the development of textmining methods for analyzing current trends are in demand.

• Asia pacific journal of information systems
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• v.26 no.4
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• pp.577-592
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• 2016

The proliferation of mobile applications and the unique characteristics of the mobile environment have attracted significant research interest in understanding customers' purchasing behaviors in mobile commerce. In this study, we extend customer value theory by combining the predictors of product performance with customer value framework to investigate how in-store information creates value for customers and influences mobile application downloads. Using a data set collected from the Google Application Store, we find that customers value both text and non-text information when they make downloading decisions. We apply latent semantic analysis techniques to analyze customer reviews and product descriptions in the mobile application store and determine the embedded valuable information. Results show that, for mobile applications, price, number of raters, and helpful information in customer reviews and product descriptions significantly affect the number of downloads. Conversely, average rating does not work in the mobile environment. This study contributes to the literature by revealing the role of in-store information in mobile application downloads and by providing application developers with useful guidance about increasing application downloads by improving in-store information management.

• Journal of The Korean Association For Science Education
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• v.40 no.1
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• pp.41-50
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• 2020

Looking at the understanding of scientific concepts from a linguistic perspective, it is very important for students to develop a deep and sophisticated understanding of words used in scientific concept as well as the ability to use them correctly. This study intends to provide the basis for productive knowledge education of scientific words by noting that the foundation of productive knowledge teaching on scientific words is not well established, and by exploring ways to teach the relationship among words that constitute scientific concept in a productive and effective manner. To this end, we extracted the relationship among the words that make up the scientific concept from the text of science textbook by using quantitative text analysis methods, second, qualitatively examined the meaning of the word relationship extracted as a result of each method, and third, we proposed a writing activity method to help improve the productive knowledge of scientific concept words. We analyzed the text of the "Force and motion" unit on first grade science textbook by using four methods of quantitative linguistic analysis: word cluster, co-occurrence, text network analysis, and word-embedding. As results, this study suggests four writing activities, completing sentence activity by using the result of word cluster analysis, filling the blanks activity by using the result of co-occurrence analysis, material-oriented writing activities by using the result of text network analysis, and finally we made a list of important words by using the result of word embedding.

• Journal of Korea Trade
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• v.24 no.8
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• pp.171-188
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• 2020

Purpose - This is an exploratory study that aims to apply text mining techniques, which computationally extracts words from the large-scale text data, to legal documents to quantify trade claim contents and enables statistical analysis. Design/methodology - This is designed to verify the validity of the application of text mining techniques as a quantitative methodology for trade claim studies, that have relied mainly on a qualitative approach. The subjects are 81 cases of arbitration and court judgments from China published on the website of the UNCITRAL where the CISG was applied. Validation is performed by comparing the manually analyzed result with the automatically analyzed result. The manual analysis result is the cluster analysis wherein the researcher reads and codes the case. The automatic analysis result is an analysis applying text mining techniques to the result of the cluster analysis. Topic modeling and semantic network analysis are applied for the statistical approach. Findings - Results show that the results of cluster analysis and text mining results are consistent with each other and the internal validity is confirmed. And the degree centrality of words that play a key role in the topic is high as the between centrality of words that are useful for grasping the topic and the eigenvector centrality of the important words in the topic is high. This indicates that text mining techniques can be applied to research on content analysis of trade claims for statistical analysis. Originality/value - Firstly, the validity of the text mining technique in the study of trade claim cases is confirmed. Prior studies on trade claims have relied on traditional approach. Secondly, this study has an originality in that it is an attempt to quantitatively study the trade claim cases, whereas prior trade claim cases were mainly studied via qualitative methods. Lastly, this study shows that the use of the text mining can lower the barrier for acquiring information from a large amount of digitalized text.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.66-77
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• 2021

Companies widely use survey to identify customer requirements, but the survey has some problems. First of all, the response is passive due to pre-designed questionnaire by companies which are the surveyor. Second, the surveyor needs to have good preliminary knowledge to improve the quality of the survey. On the other hand, text mining is an excellent way to compensate for the limitations of surveys. Recently, the importance of online review is steadily grown, and the enormous amount of text data has increased as Internet usage higher. Also, a technique to extract high-quality information from text data called Text Mining is improving. However, previous studies tend to focus on improving the accuracy of individual analytics techniques. This study proposes the methodology by combining several text mining techniques and has mainly three contributions. Firstly, able to extract information from text data without a preliminary design of the surveyor. Secondly, no need for prior knowledge to extract information. Lastly, this method provides quantitative sentiment score that can be used in decision-making.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.112-121
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• 2020

Recently, as the internet usage is increasing, accordingly generated text data is also increasing. Because this text data on the internet includes users' comments, the text data on the Internet can help you get users' opinion more efficiently and effectively. The topic of text mining has been actively studied recently, but it primarily focuses on either the content analysis or various improving techniques mostly for the performance of target mining algorithms. The objective of this study is to propose a novel method of analyzing the user's requirements by utilizing the text-mining technique. To complement the existing survey techniques, this study seeks to present priorities together with efficient extraction of customer requirements from the text data. This study seeks to identify users' requirements, derive the priorities of requirements, and identify the detailed causes of high-priority requirements. The implications of this study are as follows. First, this study tried to overcome the limitations of traditional investigations such as surveys and VOCs through text mining of online text data. Second, decision makers can derive users' requirements and prioritize without having to analyze numerous text data manually. Third, user priorities can be derived on a quantitative basis.